Image retrieval based on non-uniform bins of color histogram and dual tree complex wavelet transform

Abstract

Traditional Content-Based Image Retrieval (CBIR) systems were developed for retrieving similar kinds of images from a whole image database based on the given query image. In this paper, the authors have proposed a hierarchical approach for designing a CBIR scheme based on the color and texture features of an image. Initially, a color based approach is adopted and the intermediate results produced by using these color features is appropriate to discard a significant number of non-relevant images from the database. The intermediate database will be the input for the second stage. At this stage, a texture based approach is adopted for retrieving images from the intermediate database. The color features are extracted by computing the statistical parameters of non-uniform quantized histograms of HSV color space while a rotation invariant multi-resolution texture based approach is accomplished on value(V) component of HSV color space for extracting texture features. These texture features are extracted based on the principal texture direction and by taking the energies from various sub-bands of a dual tree complex wavelet transform (DT-CWT). Furthermore, the proposed scheme is suitable to handle mirror images during the retrieval process. The presented scheme has reduced the processing cost due to the consideration of a hierarchical approach. The proposed scheme is tested on the two well-known Corel-1K and GHIM-10K image databases respectively and satisfactory results were achieved in terms of precision, recall and F-score. The proposed scheme is compared with some other existing state of art CBIR schemes and the experimental results validate the improvement over other schemes in most of the instances.